An Improved PAM Clustering Algorithm Based on Initial Clustering Centers

Li Sheng Zhang; Mei Jie Yang; Da Jiang Lei

doi:10.4028/www.scientific.net/AMM.135-136.244

Paper Titles

An ID-Based Proxy Multi-Signature Scheme
p.217

An Identification Management Framework for IT-Enabled Services and E-Government: A Consideration of the Case of Japan
p.222

An Image Scrambling Method Based on Chaotic Sequence and Space-Bit-Plane Operation
p.231

An Improved K-Means Based Method for Fingerprint Segmentation with Sensor Interoperability
p.237

An Improved PAM Clustering Algorithm Based on Initial Clustering Centers
p.244

An Numeric Method on the Third Order Term of KDV Equation
p.253

Analysis and Design of a Wearable Robot for Lower-Limb Rehabilitation Training
p.256

Analysis of Control Strategy for Extended-Range Electric Vehicle
p.261

Analysis of Factors on Nan Pu Oilfield’s Weak Gel Flooding
p.268

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 135-136An Improved PAM Clustering Algorithm Based on...

An Improved PAM Clustering Algorithm Based on Initial Clustering Centers

Abstract:

This paper proposes a novel clustering algorithm with the steady initial center based on the PAM algorithm. As the PAM algorithm arbitrarily chooses the initial centers, this new algorithm improves the location of the initial center, which makes the initial choice much closer to the data distribution. It also overcomes the blindness of generating initial central points. The experimental results show that this algorithm is effective which performs better in classification efficiency and quality, and it also decreases the number of iterations.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 135-136)

Pages:

244-249

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.135-136.244

Citation:

Cite this paper

Online since:

October 2011

Authors:

Li Sheng Zhang, Mei Jie Yang, Da Jiang Lei

Keywords:

Cost, Dissimilarity, Medoid, Mutual Information (MI), PAM

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] J. Han and K. Micheline Data Mining . Concepts and Techniques [M]: Morgan Kauffman, (2001) 348-353.

Google Scholar

[2] Raymond T. Ng, Jiawei Han. Efficient and Effective Clustering Methods for Spatial Data Mining [C] . Proceedings of the 20th VLDB Conference Santiago, Chile, (1994) 144-155.

Google Scholar

[3] Yifeng Xu. An improved K-means clustering algorithm [J]. Computer Science and Software 25 (2008) 276-277.

Google Scholar

[4] Jolla L . Alternatives to the k-means algorithm that find better clustering [C]. In: Proceeding of ACM SIGMOD, (1992) 192-195.

Google Scholar

[5] R. Liu and H. Zhang. Segmentation of 3D meshes through spectral clustering[C]. In Proc. Paci_c Graphics, (2004) 298-305.

Google Scholar

[6] Kaufman, Leonard; Rousseeuw, Peter J. Finding groups in data：An Introduction to Cluster Analysis Wiley Series in Probability and Mathematical Statistics [J]. Applied Probability and Statistics, New York: Wiley, (1990).

DOI: 10.1002/9780470316801

Google Scholar

[7] DataSource: http: /d. download. csdn. net/down/756011/realzheng.

Google Scholar

[8] Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms [M]. Plenum Press, New York, (1981) 180-240.

Google Scholar

[9] Huizhe Zhang, Jian Wang. improve the FCM cluster algorithm based on the selected initial cluster centers [J]. Computer Science, 36 (2009) 206-208.

Google Scholar

[10] S. Zhong and J. Ghosh. A unified framework for model-based clustering. Journal of Machine Learning Research, 4 (2003) 1001-1037.

Google Scholar